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Bayesian hierarchical models for disease mapping applied to contagious pathologies.

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This study adapts Bayesian disease mapping for rare infectious diseases. Negative binomial models effectively handle overdispersion and spatial dependencies, outperforming Poisson models for accurate risk mapping.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Disease mapping traditionally models non-infectious diseases using Bayesian inference.
  • Existing methods have been enhanced to incorporate temporal data, missing values, and covariates.
  • Rare infectious diseases present unique challenges due to transmission dynamics and local overdispersion.

Purpose of the Study:

  • To adapt and propose Bayesian disease mapping methodologies for rare infectious diseases.
  • To develop specific and generic variants of disease mapping models for contagious diseases.
  • To improve the accuracy of disease risk maps for infectious diseases.

Main Methods:

  • Employed Bayesian hierarchical models, testing 60 variants.
  • Utilized Negative Binomial distribution to address local overdispersion.
  • Incorporated Conditional AutoRegressive (CAR) processes for spatial dependencies and specific spatio-temporal links for transmission dynamics.

Main Results:

  • Negative binomial models demonstrated superior performance over Poisson models with Gaussian noise in handling overdispersion.
  • Conditional AutoRegressive (CAR) processes proved relevant for modeling risk structure.
  • Generated accurate disease risk maps for simulated data and bovine tuberculosis, aligning with known risk and epidemiological knowledge.

Conclusions:

  • The adapted Bayesian disease mapping approach is effective for rare infectious diseases.
  • Negative binomial regression and CAR processes are crucial for modeling infectious disease spread and risk.
  • The study provides a robust framework for creating reliable epidemiological maps for contagious diseases.